Open Educational Resources (OER) is terminology that refers to educational resources (content and software) distributed through the Internet, free of charge and freely accessible, expanding learning opportunities for adult learners. This terminology first appeared around 2002, although its roots can be traced to the open architecture of the Internet. Until recently, OER development has focused more on quantity of contents rather than quality. In this study, we have examined the factors influencing the learning intention of adult learners in the OER context. Based on the relevant literature, we have identified a number of factors influencing a learner’s intention to use e-learning content. We have also developed a questionnaire for conducting a survey on such influencing factors. The survey results show that ease of use and relation to immediate workplace needs affect the intention of adult learners in using OER. The findings of this study can inform those developing and designing a learning environment that employs OER while also providing general guidance for developers and educators on how to design OER content.

Abstract:Recently, the development of the Internet of Things (IoT) applications has become more active with the emergence of low-power wide-area network (LPWAN), which has the advantages of low-power and long communication distance. Among the various LPWAN technologies, long-range wide-area network (LoRaWAN, or LoRa) is considered as the most mature technology. However, since LoRa performs uplink-oriented communication to increase energy efficiency, there is a restriction on the downlink function from the network server to the end devices. In this paper, we propose cooperative downlink listening to solve the fundamental problem of LoRa. In particular, the proposed scheme can be extended to various communication models such as groupcasting and geocasting by combining with the data-centric model. Experiments also show that the proposed technology not only significantly reduces network traffic compared to the LoRa standard, but also guarantees maximum energy efficiency of the LoRa.

Objective : The purpose of this study was to evaluate the efficacy of a transforaminal suprapedicular approach, semi-rigid flexible curved probe, and 3-dimensional reconstruction computed tomography (3D-CT) with discogram in the endoscopic treatment of non-contained lumbar disc herniations. Methods : The subjects were 153 patients with difficult, non-contained lumbar disc herniations undergoing endoscopic treatment. The types of herniation were as follows : extraforaminal, 17 patients; foraminal, 21 patients; high grade migration, 59 patients; and high canal compromise, 56 patients. To overcome the difficulties in endoscopic treatment, the anatomic structures were analyzed by 3D reconstruction CT and the high grade disc was extracted using a semi-rigid flexible curved probe and a transforaminal suprapedicular approach. Results : The mean follow-up was 18.3 months. The mean visual analogue scale (VAS) of the patients prior to surgery was 9.48, and the mean postoperative VAS was 1.63. According to Macnab's criteria, 145 patients had excellent and good results, and thus satisfactory results were obtained in 94.77% cases. Conclusion : In a posterolateral endoscopic lumbar discectomy, the difficult, non-contained disc is considered to be the most important factor impeding the success of surgery. By applying a semi-rigid flexible curved probe and using a transforaminal suprapedicular approach, good surgical results can be obtained, even in high grade, non-contained disc herniations.

Abstract:The setting of standards is a critical process in educational evaluation, but it is time-consuming and expensive because it is generally conducted by an education experts group. The purpose of this paper is to find a suitable cluster validity index that considers the futures of item response data for setting cut-off scores. In this study, nine representative cluster validity indexes were used to evaluate the clustering results. Cohen's kappa coefficient is used to check the conformity between a set cut-off score using four clustering techniques and a cut-off score set by experts. We compared the cut-off scores by each cluster validity index and by a group of experts. The experimental results show that the entropy-based method considers the features of item response data, so it has a realistic possibility of applying a clustering evaluation method to the setting of standards in criterion referenced evaluation.

Abstract:The k-means is one of the most popular and widely used clustering algorithm; however, it is limited to numerical data only. The k-prototypes algorithm is an algorithm famous for dealing with both numerical and categorical data. However, there have been no studies to accelerate it. In this paper, we propose a new, fast k-prototypes algorithm that provides the same answers as those of the original k-prototypes algorithm. The proposed algorithm avoids distance computations using partial distance computation. Our k-prototypes algorithm finds minimum distance without distance computations of all attributes between an object and a cluster center, which allows it to reduce time complexity. A partial distance computation uses a fact that a value of the maximum difference between two categorical attributes is 1 during distance computations. If data objects have m categorical attributes, the maximum difference of categorical attributes between an object and a cluster center is m. Our algorithm first computes distance with numerical attributes only. If a difference of the minimum distance and the second smallest with numerical attributes is higher than m, we can find the minimum distance between an object and a cluster center without distance computations of categorical attributes. The experimental results show that the computational performance of the proposed k-prototypes algorithm is superior to the original k-prototypes algorithm in our dataset.